The present invention relates generally to the use of ultrasound to study soft biological tissue and, more particularly, to the measurement of elastic properties of the tissue.
Determining the mechanical properties of biological tissue (e.g. parameters of elasticity) is of fundamental interest in clinical diagnosis because of the correlation between the healthy or pathological state of a tissue and its stiffness. It is known that some cancers are stiffer than normal tissues. This is the basis for hand palpitation used by physicians to diagnosis these disorders as well as self breast and testicular examinations. Beyond these more rudimentary techniques, imaging modalities capable of determining the relative stiffness of various tissues can also be very beneficial to cancer diagnosis in soft biological tissue. Numerous researchers have studied this field and there are a wealth of techniques in research and a few now in practice.
In recent years, ultrasound has been used to detect spatial variations in the elastic properties of biological tissue. This capability has led to a new imaging technique known as elastography. In elastography, the most common idea is to take images of tissue at two distinct levels of mechanical compression. The data from the two compression levels are compared to determine the local strain. Under well-controlled circumstances it is even possible to calculate the modulus of elasticity given this strain field, the boundary conditions and the applied stress. However, rarely is such a controlled environment attainable and thus the strain itself is used as a surrogate for the stiffness. Strain placed on the tissue associated with the compression is then measured by evaluating the variations within the ultrasound data induced by the stress, the idea being that large strains occur for softer tissues and smaller strains for harder tissues.
It is important to estimate strains with high accuracy in elastography since clinicians' diagnoses will be directly related to those estimations. This requires a processing that fits the local variations of the strain. Traditional strain estimation has been based on tracking the relative displacement of small windows of data from one frame to another under the two different compressive loads. The strain is then estimated as the spatial derivative of this displacement. Much work has gone into the study of this type of strain imaging. One of the problems with this type of strain estimation is that it does not account for the fact that locally the signals are compressed not just shifted. So as the level of strain increases, the ability to align the signals is reduced due to de-correlation of the signals. Thus, although this technique performs well for very small deformations (0.25%-1%), it fails rapidly with increasing strain. This is because, with the physical compression of the tissue, the signal is subjected to a variation in shape which is responsible for the decorrelation noise. One known technique stretches the post-compression signal temporally by the appropriate factor prior to time delay estimation. This pre-processing has been shown to improve the correlation between the pre- and postcompression signals and it compensates for the effect of compression at low strains. However, two fundamental limits arise: first, a prior knowledge of the strain magnitude is required; second, the proper temporal stretching factor depends on the local strain and cannot be constant over the signal.
Because of these limitations associated with time delay and displacement estimation techniques, techniques have been employed that estimate strain directly from the estimation of local scaling factors. Direct strain estimators estimate the local compression or stretching of the signals and thereby get the strain directly. These algorithms are capable of dealing with higher strain levels before de-correlation prevents estimation, such as strains greater than 1%.
While these techniques are an improvement over previous time delay estimation techniques, there still is a need for improving the accuracy of the measured strain, reducing the standard deviation of the measured strain, and increasing the efficiency with which these calculations can be carried out so as to make the process real-time capable. Direct strain estimation techniques that incorporate the imaginary part of the complex correlation function when estimating strain of the ultrasound data before and after compression would further improve these areas of need.
Still further improvements can be made on existing direct strain estimation techniques. Direct strain estimations are made on a window-by-window basis, thus the relative location of the windows used is determined in part by previously estimated values of the strain. This means that errors that occur in the strain estimation can accumulate or can in some cases completely destroy all the subsequent estimations. That is, if the strain estimation value is wrong, it can move the window to an extent that corresponding data from two adjacent windows does not substantially overlap. In these cases, the subsequent estimates of the strain will also be wrong. This shows up as vertical streaks of bad estimation in a strain display. Correcting the value used for the window location is very important to avoid these large lines of bad data. Thus, a strain estimation technique that can incorporate thresholds to filter out bad strain estimates, prospectively identify possible incorrect strain estimates before processing, and normalize strain data to reduce the biasing effect of incorrect strain estimates, would result in improved strain measurements.
It would therefore be desirable to have a method and a system that estimates strain directly from the imaginary part of the complex correlation function between two ultrasound data sets before and after compression and that further filters and normalizes strain data to minimize the impact of incorrect strain estimates.